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The movie Moneyball, among many things, can be considered as the prime example of data-driven performance optimization in sports. For those who haven’t watched the movie or read the book it is based on, it depicts the story of how the Oakland Athletics’ general manager, Billy Beane, used statistical data and analytics to build a competitive team despite the team’s small budget. His team, which was assembled by analyzing individual statistics of players, data mainly acquired free, went on to have an unexpectedly prolific season and reached unprecedented heights. During the historic season of 2002, the Oakland Athletics competed with and held their own against the best teams in Major League Baseball (MLB), whose budgets far outweighed their own. The team’s achievements — the most remarkable being their famous 20-game winning streak — showed how a data-driven approach can, to a great extent, compensate for a lack of resources and enhance performance by enabling effective decision-making.

Now, more than a decade and a half after the events of Moneyball the world of sports has evolved by leaps. It has increasingly incorporated different forms of technology, the most significant being the use of big data for sports analytics. Now, with the introduction of artificial intelligence in sports, we are witnessing another wave of change from the way games are played to the way they are experienced by fans across the world.

The Use of Artificial Intelligence in Sports

There are few things in the world that cannot be quantified. E everything that can be quantified, can be predicted with precision using data analytics and artificial intelligence. The world of sports is abundant in such quantifiable elements, making it ideal for the use of artificial intelligence. The applications of artificial intelligence in sports have become a common sight in recent years. Considering the positive impact they’ve brought about through their growing capabilities, they will continue to make inroads into the realm of sports. Following are a few areas in sports where artificial intelligence is set to become a mainstay component:

Scouting and Recruitment

Although humans are far from being evaluable using objective, quantitative metrics, their performances can definitely be subject to quantitative scrutiny. Sports teams, be it baseball, soccer or any other game, are increasingly using players’ individual performance data as a measure of fit and potential. However, the performance data used for scouting potential recruits doesn’t mean just using the openly known stats like home runs, goals, or passes, but using more complex metrics that take into account multiple factors. However, the perceptual limitations of humans can keep them from accurately recording and assessing these metrics. With the entry of big data and artificial intelligence in sports management, the process of recording and measuring these indicators of future success is becoming easier and more reliable.

AI can use historical data, which in sports is well documented, to predict the future potential of players before investing in them. It can also be used to estimate players’ market values to make the right offers while acquiring new talent.

Training and Performance Analysis

Like mentioned above, using general metrics such as runs scored passes made, goals scored, etc. isn’t the best way to accurately assess performances, both individual and collective. To gauge performances in any sport, analysts and coaches need to analyze a multitude of data points pertaining to individual players and collective performances. This helps them to identify the areas where players excel and those where they lag. Depending on the role of the individual players on a team, the metrics to assess their contribution varies. For instance in soccer, the key performance indicators of forwards or offensive players are different from those of midfielders (creative players) and defenders (defensive players). Although not all aspects of performance can be quantified (as of now), a growing part of a player’s game is becoming quantifiable and hence, measurable. This is possible by using artificial intelligence to derive correlations between qualitative traits and quantitative variables, and then measuring those variables to predict the corresponding qualitative value of players.

AI can also be used to identify patterns in opponents’ tactics, strengths and weaknesses while preparing for games. This helps coaches to devise detailed gamelans based on their assessment of the opposition and maximize the likelihood of victory.

Maintaining Health, Fitness and Safety

It is a well-known fact that the introduction of AI is transforming the healthcare industry in different ways. The extraordinary predictive and diagnostic capabilities of AI can also be applied in the realm of sports, where physical health and fitness is of prime importance. Since the essence of sports is the maintenance of peak physical condition, sports teams invest heavily in the physical and mental well-being of their players. In the pursuit of ensuring their players’ health and fitness, they are increasingly incorporating technological tools in player healthcare. AI has become the latest tool in these teams’ medical kits. Players regularly undergo physical tests that use AI to analyze various health parameters and player movements to evaluate their fitness and can even detect early signs of fatigue or stress-induced injuries. This can help the medical teams of sporting organizations to maintain their players’ fitness and keep them safe from injuries by taking timely action.

Many leading teams use wearable technology to track players’ movements and physical parameters during practice to help them keep track of overall player health. AI systems can be used to constantly analyze the stream of data collected by these wearables to identify the signs that are indicative of players developing musculoskeletal or cardiovascular problems. This will enable sports teams to maintain their most valuable assets in prime condition through long competitive seasons.

Broadcasting and Streaming

In addition to revolutionizing sports for the players and sports managers, AI can also revolutionize live broadcasting and impact the way the audience experiences sports. AI is also set to change the way broadcasters monetize sporting events. Based on the events on the field, AI systems can be used for automatically choosing the right camera angle to display on the viewers’ screens. It can automatically provide subtitles for live events in different languages based on the viewer’s location and language preferences. Artificial intelligence systems can also be used to identify the right opportunities to present ads based on crowd excitement levels in sporting arenas, enabling broadcasters to effectively utilize monetization opportunities through ad sales.

There is no doubt that the use of artificial intelligence in sports will make the prediction of outcomes of competitions more certain and reliable. Regardless of how much we try to bring predictability and certainty into sports, there will always be that element of unpredictability and surprise in it, by virtue of the human element. After all, that is what makes sports exciting and fascinating for audiences from across the world. As long as sports remain a fascination for the masses, businesses will always have the opportunity to profit from it. As long as there is profit to be gained from the world of sports, the investment in and incorporation of technology for sports will continue.

Naveen Joshi, columnist, is Founder and CEO of Allerin, which develops engineering and technology solutions focused on optimal customer experiences. Naveen works in AI,

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Naveen Joshi, columnist, is Founder and CEO of Allerin, which develops engineering and technology solutions focused on optimal customer experiences. Naveen works in AI, Big Data, IoT and Blockchain. An influencer with a half a million followers, he is a highly seasoned professional with more than 20 years of comprehensive experience in customizing open source products for cost optimizations of large scale IT deployment.